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A novel method for estimating functional connectivity from EEG coherence potentials. [PDF]
Sci RepPuthanmadam Subramaniyam N +1 more
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Excitable Membranes and Neural Conduction
2013Neural information processing is based on three cellular mechanisms, i.e., the excitability of neural membranes, the spatio-temporal integration of activities on dendritic trees, and synaptic transmission. The basic element of neural activity is the action potential, which is a binary event, being either present or absent, much as the electrical ...
H. Mallot
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Neural conduction and excitability following a simple warm up
Journal of Science and Medicine in Sport, 2012This study examined the effect of a generic, active warm up on neural and muscular conduction time.Single group, pre-post design.Central and peripheral neuromuscular conduction time was quantified in the abductor pollicis brevis (APB) and gastrocnemius muscles of 18 healthy participants (mean age 25.9±5.8 years, 12 males) using transcranial magnetic ...
Pearce, Alan J. +2 more
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Neural conduction in the visual pathways in ocular hypertension and glaucoma [PDF]
Graefe's Archive for Clinical and Experimental Ophthalmology, 1997 The aim of our work was to evaluate neural conduction in visual pathways in subjects with ocular hypertension and glaucoma.We assessed simultaneous recordings of pattern electroretinograms (PERG) and visual evoked potentials (VEP) in 16 subjects with ocular hypertension (OHT), in 16 subjects with primary open-angle glaucoma (POAG) and in 15 age-matched
V. Parisi
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Neural conduction of the Myo-Monitor stimulus: A quantitative analysis
The Journal of Prosthetic Dentistry, 1975With the introduction of the Myo-Monitor to dentistry, the question has arisen whether the stimulus is neurally mediated or results from direct depolarization of only the fibers of the masseter muscle. Intensity-duration curves recorded for 10 subjects quantified the relationship between stimulus intensity and the duration of the stimulus required to ...
Bernard Jankelson +3 more
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Journal of Applied Physics, 2023
In this paper, we aim to numerically resolve linear and nonlinear transient heat conduction problems in multilayer composite materials using a deep learning method called multi-domain physics-informed neural networks (MDPINNs).
Benrong Zhang, Fajie Wang, Lin Qiu
semanticscholar +1 more source
In this paper, we aim to numerically resolve linear and nonlinear transient heat conduction problems in multilayer composite materials using a deep learning method called multi-domain physics-informed neural networks (MDPINNs).
Benrong Zhang, Fajie Wang, Lin Qiu
semanticscholar +1 more source
The Physics of Fluids, 2022
In this paper, a novel deep learning technique, called multi-domain physics-informed neural network (M-PINN), is presented to solve forward and inverse problems of steady-state heat conduction in multilayer media.
Benrong Zhang +4 more
semanticscholar +1 more source
In this paper, a novel deep learning technique, called multi-domain physics-informed neural network (M-PINN), is presented to solve forward and inverse problems of steady-state heat conduction in multilayer media.
Benrong Zhang +4 more
semanticscholar +1 more source